#aspnetcore — Public Fediverse posts on home.social

That while + Task.Delay loop is quietly drifting. This drop swaps in PeriodicTimer to keep background jobs on time and spotlights IdentityServer rate limits, cleaner Minimal APIs, and YARP as a lean gateway. #dotnet #csharp #aspnetcore

.NET drip - Saturday, May 9, 2...

#aspnetcore #csharp #dotnet

Morning Dew by Alvin Ashcraft @[email protected] · 2026-05-11 · 11:10 UTC

Dew Drop – May 11, 2026 (#4665)

https://fed.brid.gy/r/https://alvinashcraft.com/2026/05/11/dew-drop-may-11-2026-4665/

#dailylinks #development #net #net11 #net52 #netmaui

Scott Galloway @[email protected] · 2026-05-09 · 11:03 UTC

Part 3 of the StyloBot Release Series is up.

https://www.mostlylucid.net/blog/stylobot-release-reliability

This one is less about bots and more about the reality of long-running .NET based systems: everything that learns from traffic eventually accumulates.

Came from one of my periodic reliability reviews where StyloBot’s vector layer had drifted to 13GB on the .NET Large Object Heap due to the wrong abstraction (in-process HNSW behaving like an unbounded cache).

The interesting part wasn’t the fix. It was recognising that the architecture itself was wrong for the runtime pattern.

Covers:

how I periodically review long-running services

using dotnet-counters, dotMemory and dotTrace to find growth

why “just add a cap” is often the wrong answer

replacing unbounded ANN structures with bounded hot caches + compacted persistence

taking the vector layer from 13GB LOH to <6MB

The broader point applies to any system that “remembers”:

bot detection, fraud scoring, recommendations, anomaly detection, RAG pipelines, adaptive systems.

Fix the shape, not the symptom.

#dotnet #aspnetcore #performance #architecture #ai #rag #observability

#dotnet #aspnetcore #performance #architecture #ai #rag

Scott Galloway @scottgal · 2026-05-09 · 11:03 UTC

Part 3 of the StyloBot Release Series is up.

https://www.mostlylucid.net/blog/stylobot-release-reliability

This one is less about bots and more about the reality of long-running .NET based systems: everything that learns from traffic eventually accumulates.

Came from one of my periodic reliability reviews where StyloBot’s vector layer had drifted to 13GB on the .NET Large Object Heap due to the wrong abstraction (in-process HNSW behaving like an unbounded cache).

The interesting part wasn’t the fix. It was recognising that the architecture itself was wrong for the runtime pattern.

Covers:

how I periodically review long-running services

using dotnet-counters, dotMemory and dotTrace to find growth

why “just add a cap” is often the wrong answer

replacing unbounded ANN structures with bounded hot caches + compacted persistence

taking the vector layer from 13GB LOH to <6MB

The broader point applies to any system that “remembers”:

bot detection, fraud scoring, recommendations, anomaly detection, RAG pipelines, adaptive systems.

Fix the shape, not the symptom.

#dotnet #aspnetcore #performance #architecture #ai #rag #observability

#dotnet #aspnetcore #performance #architecture #ai #rag

Scott Galloway @[email protected] · 2026-05-09 · 11:03 UTC

Part 3 of the StyloBot Release Series is up.

https://www.mostlylucid.net/blog/stylobot-release-reliability

This one is less about bots and more about the reality of long-running .NET based systems: everything that learns from traffic eventually accumulates.

Came from one of my periodic reliability reviews where StyloBot’s vector layer had drifted to 13GB on the .NET Large Object Heap due to the wrong abstraction (in-process HNSW behaving like an unbounded cache).

The interesting part wasn’t the fix. It was recognising that the architecture itself was wrong for the runtime pattern.

Covers:

how I periodically review long-running services

using dotnet-counters, dotMemory and dotTrace to find growth

why “just add a cap” is often the wrong answer

replacing unbounded ANN structures with bounded hot caches + compacted persistence

taking the vector layer from 13GB LOH to <6MB

The broader point applies to any system that “remembers”:

bot detection, fraud scoring, recommendations, anomaly detection, RAG pipelines, adaptive systems.

Fix the shape, not the symptom.

#dotnet #aspnetcore #performance #architecture #ai #rag #observability

#observability #rag #ai #architecture #performance #aspnetcore

Scott Galloway @[email protected] · 2026-05-09 · 11:03 UTC

Part 3 of the StyloBot Release Series is up.

https://www.mostlylucid.net/blog/stylobot-release-reliability

This one is less about bots and more about the reality of long-running .NET based systems: everything that learns from traffic eventually accumulates.

Came from one of my periodic reliability reviews where StyloBot’s vector layer had drifted to 13GB on the .NET Large Object Heap due to the wrong abstraction (in-process HNSW behaving like an unbounded cache).

The interesting part wasn’t the fix. It was recognising that the architecture itself was wrong for the runtime pattern.

Covers:

how I periodically review long-running services

using dotnet-counters, dotMemory and dotTrace to find growth

why “just add a cap” is often the wrong answer

replacing unbounded ANN structures with bounded hot caches + compacted persistence

taking the vector layer from 13GB LOH to <6MB

The broader point applies to any system that “remembers”:

bot detection, fraud scoring, recommendations, anomaly detection, RAG pipelines, adaptive systems.

Fix the shape, not the symptom.

#dotnet #aspnetcore #performance #architecture #ai #rag #observability

#dotnet #aspnetcore #performance #architecture #ai #rag

Scott Galloway @[email protected] · 2026-05-09 · 11:03 UTC

Part 3 of the StyloBot Release Series is up.

https://www.mostlylucid.net/blog/stylobot-release-reliability

This one is less about bots and more about the reality of long-running .NET based systems: everything that learns from traffic eventually accumulates.

Came from one of my periodic reliability reviews where StyloBot’s vector layer had drifted to 13GB on the .NET Large Object Heap due to the wrong abstraction (in-process HNSW behaving like an unbounded cache).

The interesting part wasn’t the fix. It was recognising that the architecture itself was wrong for the runtime pattern.

Covers:

how I periodically review long-running services

using dotnet-counters, dotMemory and dotTrace to find growth

why “just add a cap” is often the wrong answer

replacing unbounded ANN structures with bounded hot caches + compacted persistence

taking the vector layer from 13GB LOH to <6MB

The broader point applies to any system that “remembers”:

bot detection, fraud scoring, recommendations, anomaly detection, RAG pipelines, adaptive systems.

Fix the shape, not the symptom.

#dotnet #aspnetcore #performance #architecture #ai #rag #observability